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https://github.com/yevheniidatsenko/goit-cs-hw-05
💾 Hometask - Computer Systems (Asynchronous Processing)
https://github.com/yevheniidatsenko/goit-cs-hw-05
async-await asynchronous-programming asyncio goit-cs-hw-05 python3
Last synced: 10 days ago
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💾 Hometask - Computer Systems (Asynchronous Processing)
- Host: GitHub
- URL: https://github.com/yevheniidatsenko/goit-cs-hw-05
- Owner: yevheniidatsenko
- Created: 2024-12-14T09:27:50.000Z (14 days ago)
- Default Branch: main
- Last Pushed: 2024-12-14T21:31:29.000Z (13 days ago)
- Last Synced: 2024-12-14T22:25:15.170Z (13 days ago)
- Topics: async-await, asynchronous-programming, asyncio, goit-cs-hw-05, python3
- Language: Python
- Homepage:
- Size: 3.34 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Homework for the Module "Asynchronous Processing"
## Technical Task Description
The homework consists of two independent tasks.
---
### Task 1
Write a Python script that reads all files in a user-specified source folder and organizes them into subfolders in a target folder based on their file extensions. The script must perform sorting asynchronously to efficiently handle a large number of files.
#### Step-by-Step Instructions
1. Import the necessary asynchronous libraries.
2. Create an `ArgumentParser` object to handle command-line arguments.
3. Add the required arguments for defining the source and target folders.
4. Initialize asynchronous paths for the source and target directories.
5. Write an asynchronous function `read_folder` that recursively reads all files in the source folder and its subfolders.
6. Write an asynchronous function `copy_file` that copies each file to the corresponding subfolder in the target folder based on its file extension.
7. Set up error logging.
8. Run the asynchronous `read_folder` function in the main block.
**Acceptance Criteria**
- The code performs asynchronous reading and copying of files.
- Files are organized into subfolders based on their extensions.
- The program processes command-line arguments.
- All errors are logged.
- The code is readable and adheres to PEP 8 standards.
#### Result - Task 1
---
### Task 2
Write a Python script that downloads text from a given URL, analyzes word frequency in the text using the MapReduce paradigm, and visualizes the top words with the highest frequency.
#### Step-by-Step Instructions
1. Import the necessary modules (e.g., `matplotlib` and others).
2. Use the MapReduce implementation from the lecture notes.
3. Create a `visualize_top_words` function for visualizing the results.
4. In the main code block:
- Fetch the text from the given URL.
- Apply MapReduce to analyze word frequency.
- Visualize the results.
**Acceptance Criteria**
- The code fetches text from a given URL.
- It analyzes word frequency using MapReduce.
- The visualization displays top words by frequency.
- The code employs multithreading.
- The code is readable and adheres to PEP 8 standards.
#### Result - Task 2
